Adaptively weighted difference model of anisotropic and isotropic total variation for image denoising
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper proposes a novel nonconvex regularization functional by using an adaptively weighted difference model of anisotropic and isotropic total variation.By choosing the weights adaptively at each pixel, our model can enhance the anisotropic diffusion so as to achieve robust image recovery.Regarding to numerical implementations, we express the proposed model into a saddle point problem with the help of a dual formulation of the total variation, followed by a primal dual method to find a model solution.Numerical experiments demonstrate that the proposed approach is superior over several gradient-based methods for image denoising in terms of both visual appearance and quantitative metrics of signal noise ratio (SNR) and structural similarity index measure (SSIM).
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it